AI Base Caller Compression with Soft Labels for FPGA Deployment
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Solution Overview
Problem
Existing deep neural networks for base calling on mobile devices face challenges in terms of resource requirements and execution time, making it difficult to deploy them efficiently on limited computational platforms like FPGAs.
Innovation Solution
The technology employs knowledge distillation to compress a larger, teacher base caller into a smaller, student base caller by using soft labels during training, reducing the number of processing modules and parameters, enabling efficient execution on resource-constrained devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger deep neural network model is used for base calling, then accuracy is improved, but computational resource requirements and execution time increase
Solution Approach 1:
The patent creates a student model that copies the architectural structure and learned knowledge from a teacher model. The student model replicates the essential base calling functionality while using fewer parameters and computational resources, effectively creating a simplified copy that maintains accuracy on constrained devices
Solution Approach 2:
The patent transforms the model by changing parameters from hard labels (discrete category assignments) to soft labels (continuous probability distributions). This parameter transformation allows the student model to capture nuanced relationships and achieve comparable accuracy with reduced model size by learning from the teacher's probability outputs rather than discrete labels
2Measurement precision
If a larger deep neural network model is used for base calling, then accuracy is improved, but execution time increases
Solution Approach 1:
The student model copies the essential base calling capabilities from the teacher model but with a compressed architecture that executes faster. By replicating only the critical knowledge in a smaller structure, the system achieves rapid base calling on mobile devices without sacrificing accuracy
Solution Approach 2:
The teacher model performs preliminary training and knowledge extraction before the student model is deployed. The teacher model pre-processes the learning by generating soft labels that encode complex patterns, allowing the student model to inherit this pre-computed knowledge and execute predictions quickly without needing to perform complex computations in real-time
3Device complexity
If knowledge distillation with soft labels is used, then model compression is achieved, but training complexity increases
Solution Approach 1:
The teacher model serves as an intermediary that translates complex training patterns into soft label probability distributions. This intermediary transforms the training process by providing the student model with enriched supervision signals that encode nuanced relationships, simplifying the student's learning task despite the added sophistication of soft labels
4Device complexity
If the number of processing modules and parameters is reduced, then resource requirements are decreased, but model accuracy may deteriorate
Solution Approach 1:
The student model copies the teacher model's architectural blueprint and knowledge patterns but implements them with fewer parameters. By replicating the essential computational pathways and knowledge representations in a compressed form, the student maintains accuracy while reducing parameter count for deployment on resource-constrained devices
Solution Approach 2:
The transformation from hard to soft labels changes the parameter representation from discrete categories to continuous probability distributions. This parameter change allows the student model to capture more information per parameter, achieving comparable accuracy with fewer parameters by learning from the teacher's probabilistic outputs that encode uncertainty and nuanced relationships
Data Source
AI summary
The technology disclosed compresses a larger, teacher base caller into a smaller, student base caller. The student base caller has fewer processing modules and parameters than the teacher base caller. The teacher base caller is trained using hard labels (e.g., one-hot encodings). The trained teacher base caller is used to generate soft labels as output probabilities during the inference phase. The soft labels are used to train the student base caller.